25 results about "Impact crater" patented technology

The invention discloses a cross-scale planet impact crater detection method and system, and belongs to the technical field of remote sensing image intelligent identification. The method comprises the following steps: collecting and preprocessing a planet surface remote sensing image, and constructing an impact crater detection data set; a Transform-based detection model is constructed, the model comprises a multi-scale attention gating fusion feature extraction backbone module, a small target feature enhancement pyramid module and a target detection module, and the detection capability of the small-size impact crater is improved through multi-scale feature fusion and detail enhancement; pre-training the model by using the universal target detection data, and performing fine tuning optimization on the impact crater data; and finally deploying the model to an edge computing device or a remote sensing terminal to realize real-time detection. The method solves the problem of insufficient precision in cross-scale and weak texture impact crater detection of a traditional method, has the advantages of end-to-end and no need of post-processing, and is suitable for automatic image analysis of planet surfaces such as the moon and the Mars.

This invention designs a multi-scale star surface impact crater detection method, system, and medium. The method includes the following steps: S1: Acquire star surface images and segment them into image patches of different scales; S2: Construct multiple impact crater detection sub-models; S3: Train each sub-model using star surface image patches of different scales; S4: Input the segmented star surface image to be detected into each pre-trained sub-model to obtain the detection results; S5: Select the bounding boxes with high confidence and large area among all candidate bounding boxes of each impact crater as the final detection result for that impact crater. This invention can simultaneously identify impact craters of different scales in star surface images with high accuracy, especially improving the accuracy and efficiency of identifying small impact craters, providing more accurate and comprehensive data support for lunar science research, and promoting the in-depth development of lunar exploration and geological research.

The invention discloses a lunar surface four-footed impact crater detector target following method and system. The system mainly comprises a four-footed mobile platform, a binocular vision module and a data processing and control center. The binocular vision module collects images in real time, and the data processing and control center identifies and positions a front scientific detection platform target by using a deep learning algorithm; resolving a relative distance and an azimuth angle with a target in real time by combining a stereoscopic vision ranging technology; finally, a visual servo control algorithm is adopted, the calculated distance and azimuth error is converted into a control instruction, and the four-footed mobile platform is driven to adjust motion. According to the invention, artifacts and calculation amount of lunar surface reconstruction are effectively reduced, high-precision and high-reliability target perception and distance measurement in a lunar surface extreme environment are realized, the problems that a traditional wheel type platform detector is poor in trafficability in a lunar surface complex terrain environment and a target is easy to lose when a single sensor is used for tracking are solved, and the detection accuracy is improved. And the autonomy and efficiency of the detection task are improved.

The present disclosure provides a method for selecting impact craters for lunar surface dating by cluster analysis, comprising: obtaining image data of a lunar surface study area, identifying impact craters in the study area, and forming an impact crater vector data set; extracting various characteristic data of the impact craters based on the impact crater vector data, the characteristic data including grayscale image data, roughness data, TiO2 abundance data, mineral characteristic data, and spectral absorption peak characteristic data; constructing a clustering model based on the various characteristic data and a K-means ++ algorithm, classifying the impact craters of the lunar surface according to the clustering results of the various characteristic data; selecting impact craters with characteristic types meeting expectations, calculating the cumulative frequency of the impact craters, and substituting the cumulative frequency into an empirical formula for lunar impact crater dating to calculate the absolute mode age of the impact craters in the study area. This method can efficiently and intelligently classify impact craters based on a clustering model, and can effectively improve the precision of dating the geological unit in the region by selecting impact craters for statistical dating from the classified impact craters.

This invention proposes a method and system for classifying Martian impact crater degradation based on a comprehensive analysis of morphology and erosion characteristics, relating to the fields of planetary remote sensing and planetary geomorphology. The method includes acquiring high-resolution Martian remote sensing images, using a three-point circle method to outline impact craters, identifying impact craters in the study area, excluding secondary impact craters in the study area, and selecting ten descriptive factors to describe the impact craters in the study area: impact ejecta, crater rim gap, crater rim sharpness, crater rim damage degree, crater bottom filling material, sand ripple area ratio, crater bottom elevation variation coefficient, depth-to-diameter ratio, crater rim roughness, and abundance of superimposed impact craters. Based on these ten descriptive factors, the method analyzes the grading criteria for impact crater degradation, classifying the Martian impact crater degradation process into seven degradation levels. This invention classifies the degree of impact crater degradation solely based on morphological characteristics, eliminating the need for extensive and complex calculations and remaining unaffected by the heterogeneity of the pre-impact terrain and the irregularity of the impact crater morphology.

The invention relates to the technical field of numerical simulation result analysis, in particular to a feature size extraction method and device for a numerical simulation plate-shaped structure impact crater. Comprising the following steps: identifying particle set fragments of a target plate after collision, and determining a maximum fragment particle set with the maximum number of particles; performing optimization alignment on the maximum fragment particle set and the particles corresponding to the target plate before impact to determine the overall displacement and rigid rotation of the particle set of the target plate before impact, and performing superposition of the particle sets of the target plate before impact and the target plate after impact; based on the superposed particle sets of the post-impact target plate and the pre-impact target plate, identifying a particle set of an impact pit formation area; projection analysis and particle volume summation are carried out on a particle set in an impact pit forming area, the depth, diameter and volume of an impact pit are rapidly extracted, the problems of high subjectivity, large error, long consumed time and the like caused by traditional manual brute force interpretation are solved, and the analysis efficiency and reliability of a high-speed / ultra-high-speed impact large-scale parallel simulation result are greatly improved.

This application provides an automatic mapping method and system for lunar impact crater micromorphology, belonging to the field of image data processing technology. The method involves acquiring a candidate raster dataset, including lunar digital terrain data, lunar image data, and terrain-derived data; the terrain-derived data includes multiple terrain-derived factors; filtering the candidate raster dataset to determine the optimal feature combination; combining the optimal feature combination with the labels of impact crater micromorphology subclasses to construct an impact crater micromorphology subclass dataset; inputting the impact crater micromorphology subclass dataset into a pre-built deep learning model for training to obtain a target model; and using the target model to automatically extract impact crater micromorphology subclasses across the entire lunar surface, thereby completing the mapping of the entire lunar landscape. This solution alleviates the current problem of insufficient efficiency in extracting impact crater micromorphology subclasses primarily through manual extraction, providing the possibility for high-precision and rapid mapping of the entire lunar landscape.

The application provides a method and device for determining lunar soil thickness, a storage medium and a program product, relates to the technical field of planetary geology and remote sensing data processing, and is used for improving the efficiency and accuracy of determining lunar soil thickness. The method comprises the following steps: inputting a to-be-detected remote sensing image into a trained outer crater detection model of a concentric impact crater and a trained inner crater detection model of the concentric impact crater respectively, and obtaining an outer crater detection result and an inner crater detection result; wherein the outer crater detection result and the inner crater detection result both comprise center position and diameter information, the outer crater detection model is obtained based on an outer crater sample set, and the inner crater detection model is obtained based on an inner crater sample set. According to the spatial inclusion relation of the outer crater detection result and the inner crater detection result, the outer crater and the inner crater are matched, and a plurality of concentric impact crater combinations are obtained. According to the outer crater diameter, the inner crater diameter and the rest angle of each concentric impact crater combination, the lunar soil thickness corresponding to each concentric impact crater combination is determined.

The present application relates to the technical field of numerical simulation result analysis, in particular to a kind of numerical simulation plate structure impact crater feature size extraction method and device.The present application includes: identifying the particle set fragment of target plate after impact, determine the maximum fragment particle set of the most particle quantity;Maximum fragment particle set and the particle corresponding to target plate before impact are optimized and aligned to determine the overall displacement and rigid rotation of the particle set of target plate before impact, the coincidence of particle set of target plate before impact and target plate after impact is carried out;Based on the particle set of target plate after impact and target plate before impact after coincidence, identify the impact crater area particle set;Impact crater area particle set is projected and analyzed and particle volume is summed, the depth, diameter and volume of impact crater are quickly extracted, the problems such as strong subjectivity, large error and long time caused by traditional artificial brute force interpretation are broken, and the efficiency and reliability of high-speed / super high-speed impact large-scale parallel simulation result analysis are greatly improved.

The invention provides a moon impact crater automatic classification method and system, which can be applied to the technical field of computer image processing. The method solves the technical problems of low accuracy and low robustness of lunar impact crater classification in the prior art, and comprises the following steps: adaptively setting a dual-threshold parameter of an edge detection algorithm corresponding to an image signal-to-noise ratio according to the image signal-to-noise ratio, and obtaining an adaptive edge detection algorithm to carry out adaptive edge detection on a remote sensing single-channel grey-scale map; carrying out gradient detection on the remote sensing single-channel grey-scale map based on a preset first-order differential operator to obtain a fused edge image, adaptively generating a fusion weight according to an image signal-to-noise ratio, and carrying out feature extraction on the fused edge image and the remote sensing single-channel grey-scale map to obtain multi-dimensional feature data; weighted summation is carried out on the normalized multi-dimensional features according to dimension weights generated based on image signal-to-noise ratio self-adaption to obtain a freshness comprehensive score, and therefore the robustness and accuracy of moon impact crater classification are improved based on the score.

The application provides a Mars impact crater shape parameter extraction and analysis method and system based on degradation grading, and relates to the technical field of planetary remote sensing and planetary geology. The method comprises the following steps: dividing impact craters into multiple grades according to degradation degrees, generating a three-dimensional surface of the entire study area; extracting depth data of the impact craters from the overall shape of the impact craters; extracting rim height data of the impact craters from the overall shape of the impact craters; performing linear regression analysis on the depth of each degradation grade impact crater and the diameter of the impact crater, and on the rim height of each degradation grade impact crater and the diameter of the impact crater, obtaining regression parameters, and analyzing the relationship between the shape parameters of the impact craters and the degradation degrees. The application extracts shape parameters from the overall three-dimensional profile of the impact craters, and performs correlation analysis and comparison of the shape parameters of the impact craters in the same grade according to the degradation degree grade, so that the accuracy and precision are higher.

The application discloses a lunar surface point cloud obstacle semantic segmentation method, system, electronic equipment and program product, and belongs to the cross field of computer vision, deep learning and planetary exploration environment perception technology. The application introduces a boundary enhancement mechanism based on local geometric change in the point cloud feature coding stage, strengthens the feature expression of the obstacle boundary area points, and effectively alleviates the boundary information loss problem caused by random sampling. And by introducing a global context perception mechanism on the feature transmission path between the feature coding unit and the semantic decoding unit, the long-distance semantic dependence information is compensated, so that the segmentation result of the large-scale lunar surface scene is more coherent and stable. The application effectively solves the precision decline problem caused by boundary blur and global context loss in lunar surface point cloud segmentation, significantly improves the segmentation precision of obstacles such as impact craters and rocks, maintains the high efficiency of the algorithm, and is suitable for a star-borne computing platform.

This invention proposes a method and system for analyzing Martian surface degradation and erosion rates based on impact crater classification, relating to the fields of planetary remote sensing and planetary geology. The method includes classifying impact craters into degradation levels and calculating their absolute ages; calculating the average depth and average edge uplift of all impact craters at each degradation level within a defined diameter scale; calculating the impact crater degradation rate and impact crater erosion rate between any two degradation levels based on the absolute ages of impact craters at each degradation level; and fitting these curves to obtain impact crater degradation rate and impact crater erosion rate curves. This invention classifies impact craters into multiple degradation levels based on morphology and degree of damage. Combining the morphological parameters and absolute dating results of each level, it calculates two destructive factors—the surface degradation rate and erosion rate—of the Martian study area, and generates complete degradation rate and erosion rate curves that reflect the changes in the study area over time.

The invention relates to the technical field of remote sensing image processing and intelligent target identification, and discloses a Mars impact crater identification method based on multi-modal feature fusion, which comprises the following steps of: constructing a multi-modal data set by utilizing three heterogeneous remote sensing data of a Mars infrared image, a color image and a digital elevation model; preprocessing the three kinds of heterogeneous remote sensing data, and constructing a multi-scale image pyramid; three kinds of heterogeneous remote sensing data are input into three mutually independent CNN-Transform hybrid backbone networks respectively, local detail features are extracted through a convolutional structure, the Mars impact crater recognition precision is remarkably improved, the omission ratio can be effectively reduced, and the recognition accuracy of the Mars impact crater is improved by introducing a multi-modal feature fusion mechanism. The overall recognition recall rate of the Mars impact crater is remarkably improved, and the problem of serious missing detection in the prior art can be effectively relieved especially for degraded impact crater types and secondary impact crater types which are difficult to accurately recognize through a traditional method.

The invention discloses a method, a system and equipment for accurately extracting spatial information of a visual angle impact pit of a patroller and a medium. The method comprises the following steps: acquiring two-dimensional information of the impact pit; the method comprises the following steps: acquiring a binocular image, and performing information extraction on the binocular image to obtain three-dimensional information; performing coordinate transformation on the two-dimensional information of the pixel coordinate system by combining the three-dimensional information and the two-dimensional information to obtain a three-dimensional information coordinate of a camera coordinate system; according to the three-dimensional information coordinates, different impact pits are calculated to obtain space information of the impact pits, and the problem of accurate detection of the impact pits under the complex terrain and illumination conditions of the south moon is effectively solved.

The invention discloses a mars barrier impact crater extraction method in combination with an edge-guided double-branch transformer, and relates to the technical field of planet remote sensing intelligent processing and analysis, and the mars barrier impact crater extraction method in combination with the edge-guided double-branch transformer comprises the following steps: 1, obtaining mars remote sensing image data containing a barrier impact crater, segmenting the acquired data, and constructing a mask image data set required by semantic segmentation by using label making software; 2, constructing a semantic segmentation network combined with an edge-guided double-branch transformer, and dividing the network into two parts, namely an encoder and a decoder, by taking a UNet model as a skeleton; the encoder part adopts a double-branch structure and comprises an encoder taking improved ResNet50 as a skeleton, an encoder taking improved Transform as a skeleton and a feature fusion module; the decoder part comprises a plurality of decoding blocks and interacts with the encoder module of the corresponding layer through jump connection; and step 3, training and learning the features of the barrier impact craters in the barrier impact crater data set through the constructed network combined with the edge-guided double-branch transformer to obtain a final barrier impact crater extraction model. According to the invention, through the mutual cooperation of the double-encoder design, the edge-guided semantic aggregation module, the fast Fourier transform attention module and the feature fusion module, barrier collision crater extraction can be accurately carried out on the surface of the Mars, the accuracy of boundary extraction is improved, and the collision crater extraction efficiency is improved.

The application relates to a small-scale moon surface impact crater extraction method based on image space domain and frequency domain features, which comprises the following steps: acquiring LROC NAC images, and performing impact crater seed detection by using mathematical morphology to obtain impact crater seed points; based on the impact crater seed points, impact crater candidate region extraction is performed by using space domain features and based on statistical morphology constraint path set; based on the impact crater seed points, impact crater candidate region extraction is performed by using frequency domain features and based on structure consistency constraint multi-path; cross verification of the impact crater candidate regions extracted based on the space domain and the frequency domain is performed based on a difference factor, false extraction impact craters are removed, and the final impact crater extraction result is determined. Compared with the prior art, the application has the advantages of high detection result accuracy.

This application provides an automatic classification method and system for lunar impact craters, applicable to the field of computer image processing technology. This method addresses the technical problems of low accuracy and low robustness in lunar impact crater classification in related technologies. It includes: adaptively setting dual threshold parameters for an edge detection algorithm corresponding to the image signal-to-noise ratio (SNR) to obtain an adaptive edge detection algorithm for adaptive edge detection of a remote sensing single-channel grayscale image; performing gradient detection on the remote sensing single-channel grayscale image based on a preset first-order differential operator to obtain a fused edge image; adaptively generating fusion weights based on the image SNR; extracting features from the fused edge image and the remote sensing single-channel grayscale image respectively to obtain multi-dimensional feature data; and weighting and summing the normalized multi-dimensional features according to the dimensional weights adaptively generated based on the image SNR to obtain a comprehensive freshness score, thereby improving the robustness and accuracy of lunar impact crater classification.

The invention discloses a single-particle erosive wear online monitoring device based on a three-dimensional holographic technology, relates to the field of particle erosion, and is used for measuring the relationship between single-particle erosion and impact crater morphology, depth and erosive wear rate under different working conditions. According to the technical scheme, the device comprises a metal sample heating and oxidizing device used for heating a metal sample and setting the heating temperature and time; the impact particle impact speed measuring system comprises a single particle emitter used for emitting single particles and a single particle erosion impact speed measuring assembly used for measuring and recording the single particle erosion impact speed; and the surface topography measuring system is used for measuring the surface topography of the metal sample subjected to erosion-oxidation in situ by adopting interferometry.

The present application relates to the technical field of impact crater, and particularly relates to an impact crater equivalent method and device, electronic equipment and a storage medium. Initial size information of a plurality of simulated impact craters generated by impact on a target object is acquired; the initial size information includes an initial depth and an initial radius; a maximum stress value corresponding to each simulated impact crater is acquired; a target equivalent relationship corresponding to the impact simulated impact crater is generated according to the relationship between the initial depth and the initial radius and each maximum stress value. The accuracy of the generated target equivalent relationship is ensured. Further, the impact simulated impact crater is equivalent. Thus, a quantitative relationship between the size and stress characteristics of the non-standard impact simulated impact crater defect and the standard impact simulated impact crater defect can be established according to the target equivalent relationship.

The invention discloses a self-adaptive equal-height-distance optimized Mars impact crater contour line extraction method, which comprises the following steps: acquiring a Mars DEM image and carrying out data enhancement processing to obtain processed image data; performing impact crater contour extraction and optimization on the processed image data based on a Bayesian optimization algorithm to obtain an optimized impact crater contour; carrying out progressive correction on the optimized collision crater contour; and performing internal structure segmentation on the corrected impact crater bounding box based on an unsupervised SAM model in combination with the form and topological relation of the landform elements in the impact crater to complete extraction of the contour line of the Mars impact crater. According to the method, the equal height distance is adjusted in a self-adaptive mode through the Bayesian optimization algorithm, the optimal contour of the Mars impact crater and the corresponding impact crater foreground mask are finally extracted, and a foundation is laid for accurate extraction and inversion of all-around information of a high-precision geometric domain of the impact crater and an attribute domain of the high-precision impact crater.

The invention discloses a patroller multi-illumination simulation binocular image sample construction method, system and device and a medium, and the method comprises the steps: obtaining three-dimensional topographic data, and constructing a multi-illumination condition three-dimensional lunar surface simulation environment based on a digital elevation model of the south moon region according to the three-dimensional topographic data; configuring a simulation binocular camera of the patroller according to the three-dimensional lunar surface simulation environment, and simulating the pose change of the binocular camera when the patroller runs on the lunar surface terrain; and binocular image data is obtained based on the pose change, and data processing is performed on the binocular image data to obtain a simulation sample data set, so that high-trueness simulation of the lunar surface is realized, a lunar surface simulation environment containing complex topographic relief, various impact craters and illumination change is constructed, and the simulation accuracy of the lunar surface is improved. And a patroller visual angle simulation data set covering multiple scenes and multiple illumination conditions is generated.

The invention discloses a layered queriable three-dimensional scene graph construction method for star catalogue large-range detection, and belongs to the field of environment perception and mapping of planet exploration robots. The method aims to solve the problem that a navigation map and a scientific target perception map are mutually independent in an existing environment characterization method. The method comprises the following steps: generating an initial three-dimensional point cloud of rock based on a rock mask, downsampling the initial three-dimensional point cloud into sparse voxel representation, and constructing an elevation map of a local area around a robot in real time according to a depth point cloud; generating a binary trafficability grid map based on the real-time sensing data, and in the moving process of the robot, trying to connect with existing nodes by combining rocks and impact craters as new nodes so as to construct an incremental topological map; and then constructing an instance layer for storing instance information, a topology layer for passable road network information, a clustering layer for clustering adjacent scientific targets, a target center layer for storing the scientific targets or the center position of a target gathering area, and a planet exploration robot path layer for providing context for path planning and analysis.

The present application designs a star catalog impact crater detection and identification method and system, which comprises the following steps: S1: obtaining the pseudo-height spectral image of the star catalog impact crater; S2: extracting multiple features of the image as multiple pseudo-spectrum images respectively; S3: fusing and enhancing the multiple pseudo-spectrum to generate a PSEF image; S4: using the PSEF image to train a deep learning-based target detection model to obtain a trained impact crater detection model; S5: inputting the star image data to be detected into the impact crater detection model after being made into a PSEF image to obtain a detection result. Through the extraction and fusion of multi-modal features, the present application can effectively extract multiple features of small-scale impact craters under complex terrain and lighting conditions, the generated PSEF image can better adapt to the training process of the deep learning detection model, and the accuracy and robustness of the detection model for detecting small-scale lunar impact craters are improved.

This invention discloses a method for extracting Martian impact crater contours using adaptive contour interval optimization. The method includes: acquiring Martian DEM images and performing data augmentation to obtain processed image data; extracting and optimizing the impact crater contours based on a Bayesian optimization algorithm to obtain optimized impact crater contours; progressively correcting the optimized impact crater contours; and segmenting the corrected impact crater bounding box using an unsupervised SAM model combined with the morphological and topological relationships of the crater's internal landform elements to complete the extraction of the Martian impact crater contours. This invention uses a Bayesian optimization algorithm to adaptively adjust the contour interval, ultimately extracting the optimal contour of the Martian impact crater and the corresponding impact crater foreground mask, laying the foundation for high-precision mapping of the geometric domain and accurate extraction and inversion of comprehensive information in the attribute domain of impact craters.